AI Research Engineer

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About Brainwave Science:

Brainwave Science

About the Role

AI/ML Engineer – LLM & Deep Learning Expert

real-world challenges

Responsibilities

• Design, train, fine-tune, and deploy 

  Large Language Models

   using frameworks like 

  PyTorch

  , 

  TensorFlow

  , or 

  Hugging Face Transformers

   
• Integrate LLMs for 

  local/edge deployment

   using tools like 

  Ollama

  , 

  LangChain

  , 

  LM Studio

  , or 

  llama.cpp

   
• Build NLP applications for 

  intelligent automation

  , 

  investigative analytics

  , and 

  biometric interpretation

   
• Optimize models for 

  low-latency

  , 

  token efficiency

  , and 

  on-device performance

   
• Work on 

  prompt engineering

  , 

  embedding tuning

  , and 

  vector search integration

   (FAISS, Qdrant, Weaviate) 
• Collaborate with technical and research teams to deliver scalable AI-driven features 
• Stay current with developments in 

  open-source

   and 

  closed-source

   LLM ecosystems (e.g., Meta, OpenAI, Mistral) 

Must-Have Requirements

• B.Tech/M.Tech

   in 

  Computer Science (CSE)

  , 

  Electronics & Communication (ECE)

  , or 

  Electrical & Electronics (EEE)

   from 

  IIT, NIT, or BITS

   
• Minimum 

  3

  -

  4 years of hands-on experience

   in 

  AI/ML, deep learning

  , and 

  LLM development

   
• Deep experience with 

  Transformer models

   (e.g., GPT, LLaMA, Mistral, Falcon, Claude) 
• Hands-on with tools like 

  LangChain

  , 

  Hugging Face

  , 

  Ollama

  , 

  Docker

  , or 

  Kubernetes

   
• Proficiency in 

  Python

  , and strong knowledge of 

  Linux environments

   
• Strong understanding of 

  NLP

  , 

  attention mechanisms

  , and 

  model fine-tuning

   

Preferred Qualifications

• Experience with 

  biosignals

  , especially 

  EEG

   or 

  time-series data

   
• Experience deploying custom-trained LLMs on 

  proprietary datasets

   
• Familiarity with 

  RAG pipelines

   and 

  multi-modal models

   (e.g., CLIP, LLaVA) 
• Knowledge of 

  cloud platforms

   (AWS, GCP, Azure) for scalable model training and serving 
• Published research, patents, or open-source contributions in AI/ML communities 
• Excellent communication, analytical, and problem-solving skills 

What We Offer

• Competitive compensation based on experience 
• Flexible working hours and remote-friendly culture 
• Access to high-performance compute infrastructure 
• Opportunities to work on groundbreaking AI projects in healthcare, security, and defense